Agentic Workflow for Automated Vegetation Management for Fire Risk and Biodiversity

The workflow begins with an automated pipeline that ingests and aligns multi-source data: high-resolution LiDAR point clouds for structural vegetation mapping, multispectral satellite imagery (Sentinel-2, Planet) for health assessment, historical fire perimeters, and biodiversity databases (e.g., species habitat maps). Agents validate data quality, correct for atmospheric and seasonal artifacts, and create a unified geospatial layer, eliminating weeks of manual data prep and ensuring a consistent analysis baseline.

Specialized AI agents operate in parallel. A Fire Risk Agent calculates a hazard score by analyzing vegetation density, fuel moisture content (from spectral indices), proximity to ignition sources (power lines, pipelines), and historical weather patterns. A Biodiversity Agent scores the ecological value of vegetation patches by cross-referencing species data, habitat connectivity, and protected status. These scores are combined into a single, weighted priority index for each management zone.

A central orchestrator (built on frameworks like LangGraph) ingests the priority index alongside operational constraints: crew availability, budget cycles, regulatory windows (e.g., nesting seasons), and contractual SLAs. It runs optimization algorithms to generate a ranked work plan, scheduling trimming, clearing, or preservation actions. The system automatically triggers exception handling for high-risk/high-value conflicts, routing them for human review.

For every scheduled action, a documentation agent automatically assembles the required environmental compliance package. It retrieves relevant permit conditions, populates agency forms (e.g., USACE wetland permits, ESA consultation templates), and generates maps and justification narratives using RAG over project baselines and regulatory text. Drafts are routed through configured approval gates in systems like SharePoint or Procore before final submission.

Prioritized work orders and maps are pushed directly to field crew mobile apps (e.g., Fulcrum, ArcGIS Field Maps) and asset management systems (IBM Maximo, SAP). Post-action, the workflow ingests verification data—drone imagery, crew completion reports—to update the vegetation model and close the loop. This creates a living asset register, provides audit trails for regulators, and feeds data back to improve the risk models.

The entire workflow is governed by a control plane that logs every agent decision, data source, and model inference for full auditability. A dashboard provides real-time observability into system performance, priority queue status, and exception rates. Approval gates are configurable for high-stakes decisions (e.g., clearing in critical habitat), and model performance is continuously monitored for drift against new satellite and inspection data.

Primary Driver & Beneficiary. This team is accountable for wildfire prevention, reliability, and regulatory compliance (e.g., NERC FAC-003). They drive the build to replace manual, sample-based patrols with continuous, AI-powered risk assessment. The workflow automates the ingestion of LiDAR/satellite data, prioritizes trimming circuits by fire risk score, and generates defensible inspection logs. Benefits include a 40-60% reduction in patrol labor, proactive mitigation of Public Safety Power Shutoff (PSPS) triggers, and stronger audit trails for regulators.

Critical Approver & Co-Beneficiary. This function manages biodiversity commitments, ESA consultations, and habitat mitigation. They require the workflow to dual-score vegetation for both risk and ecological value, preventing unnecessary clearing of native or protected species. The build automates the generation of compliance documentation (e.g., ESA screening reports, mitigation plans) by fusing model outputs with ecological databases. Benefits include reduced permitting delays, quantifiable progress toward biodiversity net-gain goals, and lower risk of violations.

Execution Partner & Efficiency Gainer. These field crews receive AI-prioritized work orders via mobile apps, complete with species identification, risk rationale, and GPS polygons. The workflow automates schedule optimization based on crew location, equipment, and access permissions, integrating with systems like ArcGIS Field Maps or Trimble. Benefits include reduced windshield time, fewer change orders due to data discrepancies, and higher crew utilization through dynamic routing.

Architecture Enabler & Scale Owner. This team architects the data pipeline from source systems (e.g., Esri, Planet, internal LiDAR repos) to the agentic orchestration layer (e.g., LangGraph). They drive requirements for model retraining loops, API integrations with asset management systems (IBM Maximo, SAP EAM), and observability dashboards. Benefits include a centralized geospatial feature store, reduced data silos, and a scalable platform for adding new inspection types (e.g., corrosion, encroachment).

Economic Validator & ROI Measurer. This stakeholder funds the build based on a business case quantifying wildfire liability reduction, insurance premium impacts, and operational savings. The workflow provides continuous risk scoring and forecasting, enabling probabilistic modeling of incident costs. Benefits include data-driven capital allocation for grid hardening, improved underwriting data for wildfire insurance, and clear metrics on cost-avoidance from prevented ignitions.

External Facing Communicator. This group uses outputs from the workflow to demonstrate due diligence to public utility commissions, communities, and environmental groups. Automated 3D visualizations of risk zones and planned mitigation actions support public hearings. Benefits include transparent, data-backed community engagement, faster responses to information requests, and strengthened regulatory relationships through proactive disclosure.